Data¶

In [ ]:
from fiona import listlayers

ukMapsLink="https://github.com/UK-Data-Science/tarea5nicoledistancias/raw/main/ukMaps_27700.gpkg"

#layers in maps
listlayers(ukMapsLink)
Out[ ]:
['country', 'cities', 'rivers', 'centroid', 'airports']
In [ ]:
import geopandas as gpd

# asignando
country=gpd.read_file(ukMapsLink,layer='country')
cities=gpd.read_file(ukMapsLink,layer='cities')
airports=gpd.read_file(ukMapsLink,layer='airports')
rivers=gpd.read_file(ukMapsLink,layer='rivers')

Rivers & Longest rivers¶

In [ ]:
rivers.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 579476 entries, 0 to 579475
Data columns (total 15 columns):
 #   Column    Non-Null Count   Dtype   
---  ------    --------------   -----   
 0   name      111683 non-null  object  
 1   name_en   811 non-null     object  
 2   waterway  579420 non-null  object  
 3   covered   241 non-null     object  
 4   width     7080 non-null    object  
 5   depth     59 non-null      object  
 6   layer     95921 non-null   object  
 7   blockage  0 non-null       object  
 8   tunnel    102286 non-null  object  
 9   natural   109 non-null     object  
 10  water     47 non-null      object  
 11  source    252026 non-null  object  
 12  osm_id    579476 non-null  int64   
 13  osm_type  579476 non-null  object  
 14  geometry  579476 non-null  geometry
dtypes: geometry(1), int64(1), object(13)
memory usage: 66.3+ MB
In [ ]:
rivers.head(10) # falta filtrar : hay streams, drains, etc...
Out[ ]:
name name_en waterway covered width depth layer blockage tunnel natural water source osm_id osm_type geometry
0 None None drain None None None None None None None None None 330793356 ways_line LINESTRING (0.254 50.753, 0.254 50.753, 0.254 ...
1 None None drain None None None None None None None None None 330793355 ways_line LINESTRING (0.254 50.753, 0.254 50.753)
2 None None stream None None None None None None None None None 504611310 ways_line LINESTRING (0.249 50.807, 0.249 50.807)
3 None None stream None None None None None yes None None None 504611312 ways_line LINESTRING (0.249 50.807, 0.249 50.807)
4 None None stream None None None None None None None None None 504611311 ways_line LINESTRING (0.249 50.807, 0.249 50.807, 0.249 ...
5 None None stream None None None None None None None None None 505129684 ways_line LINESTRING (0.251 50.808, 0.251 50.808, 0.251 ...
6 None None stream None None None None None culvert None None None 505129685 ways_line LINESTRING (0.253 50.809, 0.253 50.809)
7 None None stream None None None None None None None None None 505129686 ways_line LINESTRING (0.253 50.809, 0.254 50.810)
8 None None stream None None None None None culvert None None None 505129687 ways_line LINESTRING (0.254 50.810, 0.254 50.810)
9 None None stream None None None None None None None None None 506124003 ways_line LINESTRING (0.236 50.811, 0.236 50.811, 0.236 ...
In [ ]:
filtered_rivers = rivers.dropna(subset=['name'])
filtered_rivers = filtered_rivers[filtered_rivers['waterway'] == 'river']
filtered_rivers
Out[ ]:
name name_en waterway covered width depth layer blockage tunnel natural water source osm_id osm_type geometry
11 Lottbridge Sewer None river None None None None None None None None NPE 552596676 ways_line LINESTRING (0.275 50.781, 0.275 50.782, 0.276 ...
12 Lottbridge Sewer None river None None None None None None None None NPE 552596679 ways_line LINESTRING (0.276 50.782, 0.276 50.782, 0.278 ...
16 Lottbridge Sewer None river None None None None None None None None NPE 1194672959 ways_line LINESTRING (0.283 50.784, 0.283 50.786, 0.284 ...
18 Lottbridge Sewer None river None None None -1 None culvert None None NPE 1194672960 ways_line LINESTRING (0.284 50.786, 0.284 50.786)
31 Lottbridge Sewer None river None None None None None None None None NPE 125744897 ways_line LINESTRING (0.284 50.786, 0.284 50.786, 0.285 ...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
579422 Kearvaig River None river None None None None None None None None OS7 553057918 ways_line LINESTRING (-4.939 58.577, -4.939 58.578, -4.9...
579425 Kearvaig River None river None None None None None None None None OS7 553057922 ways_line LINESTRING (-4.942 58.588, -4.942 58.589, -4.9...
579429 Kesgaig River None river None None None None None None None None OS7 79127379 ways_line LINESTRING (-4.986 58.565, -4.988 58.565, -4.9...
579449 Kearvaig River None river None None None None None None None None OS7 553057925 ways_line LINESTRING (-4.941 58.591, -4.942 58.592, -4.9...
579459 Kearvaig River None river None None None None None None None None OS7 553057930 ways_line LINESTRING (-4.945 58.599, -4.945 58.600, -4.9...

22006 rows × 15 columns

In [ ]:
keepColumns = ["name","geometry"] # solo las utiles
filtered_rivers = filtered_rivers[keepColumns]
filtered_rivers.columns
Out[ ]:
Index(['name', 'geometry'], dtype='object')
In [ ]:
import matplotlib.pyplot as plt

# para ver
plt.figure(figsize=(10, 10))
filtered_rivers.plot(ax=plt.gca(), color='blue', linewidth=0.5)
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
filtered_rivers.name.value_counts() # muchos con un mismo nombre
Out[ ]:
name
River Thames          546
River Avon            380
River Tove            280
River Frome           244
River Stour           227
                     ... 
Cooks Backwater         1
Garkie's Mill Lade      1
Lugate Water            1
Boondreigh Water        1
Kesgaig River           1
Name: count, Length: 3272, dtype: int64
In [ ]:
filtered_rivers.dissolve(by='name')  # de más de 20k, se reduce a 3272
Out[ ]:
geometry
name
A' Ghairbhe MULTILINESTRING ((-5.343 57.563, -5.344 57.565...
A' Phait LINESTRING (-4.886 57.610, -4.885 57.609, -4.8...
Abainn Rangail MULTILINESTRING ((-6.369 56.976, -6.369 56.976...
Abainn Srath Ascaig LINESTRING (-5.540 57.339, -5.544 57.339, -5.5...
Abberd Brook LINESTRING (-1.988 51.441, -1.989 51.441, -1.9...
... ...
avon MULTILINESTRING ((-1.708 52.184, -1.708 52.184...
e Dhomhain LINESTRING (-4.265 56.846, -4.265 56.846, -4.2...
old mill goit LINESTRING (-1.533 53.909, -1.532 53.909, -1.5...
sence MULTILINESTRING ((-1.125 52.570, -1.124 52.570...
willow Beck LINESTRING (-1.454 54.334, -1.454 54.334, -1.4...

3272 rows × 1 columns

In [ ]:
filtered_rivers_dissolved = filtered_rivers.dissolve(by='name')
rivers_4326 = filtered_rivers_dissolved.reset_index()
rivers_4326.set_crs(epsg=4326, inplace=True, allow_override=True) # de acuerdo a sus coords
rivers_27700 = rivers_4326.to_crs(27700)

Escogiendo a los ríos más largos:¶

In [ ]:
rivers_27700['length'] = rivers_27700.geometry.length
rivers_27700
Out[ ]:
name geometry length
0 A' Ghairbhe MULTILINESTRING ((200111.287 857483.501, 20011... 6485.627138
1 A' Phait LINESTRING (227709.258 861393.222, 227762.352 ... 253.797301
2 Abainn Rangail MULTILINESTRING ((134638.068 795671.479, 13462... 2114.161415
3 Abainn Srath Ascaig LINESTRING (187038.580 833163.050, 186816.943 ... 1385.630855
4 Abberd Brook LINESTRING (400906.211 171338.935, 400884.103 ... 1031.022717
... ... ... ...
3267 avon MULTILINESTRING ((420063.177 254053.014, 42006... 118.599844
3268 e Dhomhain LINESTRING (261958.609 775040.023, 261970.609 ... 4712.571418
3269 old mill goit LINESTRING (430797.097 445950.343, 430814.951 ... 347.724695
3270 sence MULTILINESTRING ((459421.864 297243.297, 45944... 3676.723847
3271 willow Beck LINESTRING (435587.433 493313.933, 435580.314 ... 1040.884424

3272 rows × 3 columns

In [ ]:
rivers_sorted = rivers_27700.sort_values(by='length', ascending=False)
longest_rivers_27700 = rivers_sorted.head(15)
longest_rivers_27700
Out[ ]:
name geometry length
2115 River Avon MULTILINESTRING ((416370.634 117850.427, 41637... 537927.236718
2761 River Thames MULTILINESTRING ((459675.517 180847.117, 45967... 350897.120340
2682 River Severn MULTILINESTRING ((373366.200 211267.798, 37329... 326450.311161
2722 River Stour MULTILINESTRING ((632950.555 158519.407, 63295... 318856.421150
2266 River Dee MULTILINESTRING ((331760.699 339517.608, 33180... 309110.609724
2853 River Wye MULTILINESTRING ((489602.377 186562.954, 48956... 292297.060518
2271 River Derwent MULTILINESTRING ((443855.667 331720.970, 44383... 283201.140930
2787 River Trent MULTILINESTRING ((398267.680 325421.202, 39827... 272575.103101
2282 River Don MULTILINESTRING ((429280.763 398799.321, 42927... 245148.653223
2758 River Test MULTILINESTRING ((435558.653 115739.584, 43556... 235050.804979
2365 River Frome MULTILINESTRING ((360223.492 96824.249, 360227... 228168.252531
2312 River Eden MULTILINESTRING ((540388.787 146717.695, 54040... 205348.214137
2397 River Great Ouse MULTILINESTRING ((518103.098 260199.826, 51809... 194204.635265
2713 River Spey MULTILINESTRING ((320115.428 841655.529, 32013... 168010.687478
2585 River Nene MULTILINESTRING ((478047.188 260040.295, 47810... 165166.228714
In [ ]:
# graficando
plt.figure(figsize=(8, 8))
longest_rivers_27700.plot(ax=plt.gca(), color='blue', linewidth=0.5)
Out[ ]:
<Axes: >
No description has been provided for this image

Seaports¶

In [ ]:
import pandas as pd

# importando
portsFileLink="https://github.com/UK-Data-Science/tarea5nicoledistancias/raw/main/UpdatedPub150.csv"
infoseaports=pd.read_csv(portsFileLink)

infoseaports.columns.to_list() # muchas columnas
Out[ ]:
['OID_',
 'World Port Index Number',
 'Region Name',
 'Main Port Name',
 'Alternate Port Name',
 'UN/LOCODE',
 'Country Code',
 'World Water Body',
 'IHO S-130 Sea Area',
 'Sailing Direction or Publication',
 'Publication Link',
 'Standard Nautical Chart',
 'IHO S-57 Electronic Navigational Chart',
 'IHO S-101 Electronic Navigational Chart',
 'Digital Nautical Chart',
 'Tidal Range (m)',
 'Entrance Width (m)',
 'Channel Depth (m)',
 'Anchorage Depth (m)',
 'Cargo Pier Depth (m)',
 'Oil Terminal Depth (m)',
 'Liquified Natural Gas Terminal Depth (m)',
 'Maximum Vessel Length (m)',
 'Maximum Vessel Beam (m)',
 'Maximum Vessel Draft (m)',
 'Offshore Maximum Vessel Length (m)',
 'Offshore Maximum Vessel Beam (m)',
 'Offshore Maximum Vessel Draft (m)',
 'Harbor Size',
 'Harbor Type',
 'Harbor Use',
 'Shelter Afforded',
 'Entrance Restriction - Tide',
 'Entrance Restriction - Heavy Swell',
 'Entrance Restriction - Ice',
 'Entrance Restriction - Other',
 'Overhead Limits',
 'Underkeel Clearance Management System',
 'Good Holding Ground',
 'Turning Area',
 'Port Security',
 'Estimated Time of Arrival Message',
 'Quarantine - Pratique',
 'Quarantine - Sanitation',
 'Quarantine - Other',
 'Traffic Separation Scheme',
 'Vessel Traffic Service',
 'First Port of Entry',
 'US Representative',
 'Pilotage - Compulsory',
 'Pilotage - Available',
 'Pilotage - Local Assistance',
 'Pilotage - Advisable',
 'Tugs - Salvage',
 'Tugs - Assistance',
 'Communications - Telephone',
 'Communications - Telefax',
 'Communications - Radio',
 'Communications - Radiotelephone',
 'Communications - Airport',
 'Communications - Rail',
 'Search and Rescue',
 'NAVAREA',
 'Facilities - Wharves',
 'Facilities - Anchorage',
 'Facilities - Dangerous Cargo Anchorage',
 'Facilities - Med Mooring',
 'Facilities - Beach Mooring',
 'Facilities - Ice Mooring',
 'Facilities - Ro-Ro',
 'Facilities - Solid Bulk',
 'Facilities - Liquid Bulk',
 'Facilities - Container',
 'Facilities - Breakbulk',
 'Facilities - Oil Terminal',
 'Facilities - LNG Terminal',
 'Facilities - Other',
 'Medical Facilities',
 'Garbage Disposal',
 'Chemical Holding Tank Disposal',
 'Degaussing',
 'Dirty Ballast Disposal',
 'Cranes - Fixed',
 'Cranes - Mobile',
 'Cranes - Floating',
 'Cranes Container',
 'Lifts - 100+ Tons',
 'Lifts - 50-100 Tons',
 'Lifts - 25-49 Tons',
 'Lifts - 0-24 Tons',
 'Services - Longshoremen',
 'Services - Electricity',
 'Services -Steam',
 'Services - Navigation Equipment',
 'Services - Electrical Repair',
 'Services - Ice Breaking',
 'Services -Diving',
 'Supplies - Provisions',
 'Supplies - Potable Water',
 'Supplies - Fuel Oil',
 'Supplies - Diesel Oil',
 'Supplies - Aviation Fuel',
 'Supplies - Deck',
 'Supplies - Engine',
 'Repairs',
 'Dry Dock',
 'Railway',
 'Latitude',
 'Longitude']
In [ ]:
# cambiar de nombre
infoseaports.rename(columns={'Main Port Name':'portName', 'Country Code':'countryCode'},inplace=True)

# quedarse con algunas
infoseaports=infoseaports.loc[:,['portName', 'countryCode','Latitude', 'Longitude']]

infoseaports.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3801 entries, 0 to 3800
Data columns (total 4 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   portName     3801 non-null   object 
 1   countryCode  3801 non-null   object 
 2   Latitude     3801 non-null   float64
 3   Longitude    3801 non-null   float64
dtypes: float64(2), object(2)
memory usage: 118.9+ KB
In [ ]:
infoseaports.head() # sigue habiendo de otros países
Out[ ]:
portName countryCode Latitude Longitude
0 Maurer United States 40.533333 -74.250000
1 Iharana Madagascar -13.350000 50.000000
2 Andoany Madagascar -13.400000 48.300000
3 Chake Chake Tanzania -5.250000 39.766667
4 Mjimwema Terminal Tanzania -6.816667 39.366667

Convertir los puntos en objetos espaciales proyectados (GDF de puntos):¶

In [ ]:
#spatial points (sin proyectar)
seaports=gpd.GeoDataFrame(data=infoseaports.copy(),
                           geometry=gpd.points_from_xy(infoseaports.Longitude,
                                                       infoseaports.Latitude),
                          crs=4326)

# quedarse con solo uk
seaports_uk=seaports[seaports['countryCode']=='United Kingdom'].copy()

# reset indexes
seaports_uk.reset_index(drop=True, inplace=True)
In [ ]:
seaports_uk.crs # no esta en el uk
Out[ ]:
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
In [ ]:
# reproyectar
seaports_uk_27700=seaports_uk.to_crs(27700)
In [ ]:
seaports_uk_27700.crs # checkeando
Out[ ]:
<Projected CRS: EPSG:27700>
Name: OSGB36 / British National Grid
Axis Info [cartesian]:
- E[east]: Easting (metre)
- N[north]: Northing (metre)
Area of Use:
- name: United Kingdom (UK) - offshore to boundary of UKCS within 49°45'N to 61°N and 9°W to 2°E; onshore Great Britain (England, Wales and Scotland). Isle of Man onshore.
- bounds: (-9.01, 49.75, 2.01, 61.01)
Coordinate Operation:
- name: British National Grid
- method: Transverse Mercator
Datum: Ordnance Survey of Great Britain 1936
- Ellipsoid: Airy 1830
- Prime Meridian: Greenwich

Small Airports¶

In [ ]:
airports.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 1142 entries, 0 to 1141
Data columns (total 8 columns):
 #   Column         Non-Null Count  Dtype   
---  ------         --------------  -----   
 0   name           1142 non-null   object  
 1   kind           1142 non-null   object  
 2   latitude_deg   1142 non-null   float64 
 3   longitude_deg  1142 non-null   float64 
 4   elevation_ft   433 non-null    float64 
 5   iso_region     1142 non-null   object  
 6   municipality   724 non-null    object  
 7   geometry       1142 non-null   geometry
dtypes: float64(3), geometry(1), object(4)
memory usage: 71.5+ KB
In [ ]:
smallAirports=airports[airports['kind']=='small_airport']  # seleccionando
smallAirports
Out[ ]:
name kind latitude_deg longitude_deg elevation_ft iso_region municipality geometry
0 Cark airfield small_airport 54.163753 -2.962299 20.0 GB-ENG Cark POINT (337266.334 474644.531)
1 Badminton Airfield small_airport 51.548417 -2.303181 495.0 GB-ENG Badminton POINT (379073.734 183324.224)
2 Boon Hill Farm Airstrip small_airport 54.308086 -0.983105 NaN GB-ENG Fadmoor, Kirbymoorside POINT (466262.484 490752.838)
3 Ledbury Airfield small_airport 52.001900 -2.474160 250.0 GB-ENG Ledbury POINT (367545.998 233824.062)
4 Milfield Glider Field small_airport 55.591636 -2.087231 NaN GB-ENG None POINT (394600.512 633114.877)
... ... ... ... ... ... ... ... ...
1136 Out Skerries Airfield small_airport 60.425200 -0.750000 76.0 GB-SCT Grunay Island POINT (468925.072 1171915.066)
1137 Papa Stour Airport small_airport 60.321701 -1.693060 75.0 GB-SCT Papa Stour Island POINT (417054.691 1159774.295)
1138 Glenforsa Airfield small_airport 56.517202 -5.914010 15.0 GB-SCT Glenforsa POINT (159326.623 742992.703)
1139 Broadford Airstrip small_airport 57.253399 -5.827900 24.0 GB-SCT Ashaig POINT (169209.414 824571.040)
1140 Plockton Airstrip small_airport 57.335499 -5.673080 226.0 GB-SCT Plockton POINT (179035.669 833188.310)

954 rows × 8 columns

Large Airports¶

In [ ]:
airports.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 1142 entries, 0 to 1141
Data columns (total 8 columns):
 #   Column         Non-Null Count  Dtype   
---  ------         --------------  -----   
 0   name           1142 non-null   object  
 1   kind           1142 non-null   object  
 2   latitude_deg   1142 non-null   float64 
 3   longitude_deg  1142 non-null   float64 
 4   elevation_ft   433 non-null    float64 
 5   iso_region     1142 non-null   object  
 6   municipality   724 non-null    object  
 7   geometry       1142 non-null   geometry
dtypes: float64(3), geometry(1), object(4)
memory usage: 71.5+ KB
In [ ]:
airports.crs # checkeando
Out[ ]:
<Projected CRS: EPSG:27700>
Name: OSGB36 / British National Grid
Axis Info [cartesian]:
- E[east]: Easting (metre)
- N[north]: Northing (metre)
Area of Use:
- name: United Kingdom (UK) - offshore to boundary of UKCS within 49°45'N to 61°N and 9°W to 2°E; onshore Great Britain (England, Wales and Scotland). Isle of Man onshore.
- bounds: (-9.01, 49.75, 2.01, 61.01)
Coordinate Operation:
- name: British National Grid
- method: Transverse Mercator
Datum: Ordnance Survey of Great Britain 1936
- Ellipsoid: Airy 1830
- Prime Meridian: Greenwich
In [ ]:
largeAirports_27700=airports[airports['kind']=='large_airport'] # seleccionando
largeAirports_27700.reset_index(drop=True, inplace=True)
In [ ]:
# graficando
base=largeAirports_27700.plot(color='red',marker="^")
No description has been provided for this image

Exercise 1 (Distance between two points)¶

  1. Use two maps of points from your country.

  2. Compute the distance matrix for both maps.

  3. Select one row of the distance matrix, and plot the two points with the minimal distance on top of the country of your choosing.

In [ ]:
seaports_uk_27700.head() # mapa de puntos 1
Out[ ]:
portName countryCode Latitude Longitude geometry
0 Mallaig United Kingdom 57.000000 -5.816667 POINT (168307.354 796350.120)
1 Lochmaddy United Kingdom 57.600000 -7.166667 POINT (91472.808 868427.897)
2 Lerwick United Kingdom 60.167000 -1.150000 POINT (447272.648 1142808.423)
3 Burghead United Kingdom 57.700000 -3.500000 POINT (310696.799 868787.732)
4 Buckie Harbor United Kingdom 57.683333 -2.966667 POINT (342456.181 866353.738)
In [ ]:
largeAirports_27700.head() # mapa de puntos 2
Out[ ]:
name kind latitude_deg longitude_deg elevation_ft iso_region municipality geometry
0 Belfast International Airport large_airport 54.657501 -6.215830 268.0 GB-NIR Belfast POINT (128198.290 537323.459)
1 Birmingham International Airport large_airport 52.453899 -1.748030 327.0 GB-ENG Birmingham POINT (417219.887 284023.644)
2 Manchester Airport large_airport 53.349375 -2.279521 257.0 GB-ENG Manchester POINT (381489.869 383645.942)
3 London Luton Airport large_airport 51.874699 -0.368333 526.0 GB-ENG Luton, Bedfordshire POINT (512423.792 220827.818)
4 London Gatwick Airport large_airport 51.148771 -0.192089 202.0 GB-ENG Gatwick, Surrey POINT (526550.011 140387.944)

Todas las posibles distancias entre los GDFs:¶

In [ ]:
seaports_uk_27700.set_index('portName').geometry.apply\
(lambda g: largeAirports_27700.set_index('name').geometry.distance(g)/1000)
Out[ ]:
name Belfast International Airport Birmingham International Airport Manchester Airport London Luton Airport London Gatwick Airport London Heathrow Airport Glasgow International Airport Edinburgh Airport London Stansted Airport
portName
Mallaig 262.113617 569.592721 464.512135 670.553536 747.411652 706.972809 151.874623 190.648460 690.897776
Lochmaddy 333.134972 669.058659 564.910116 772.389535 848.135805 807.717720 255.028475 295.893681 793.936865
Lerwick 684.412512 859.310459 762.007248 924.279670 1005.550454 968.899103 515.922378 487.405874 925.895013
Burghead 378.383760 594.387253 490.279731 678.635270 759.709720 720.285283 211.362477 194.960080 690.000669
Buckie Harbor 392.641526 587.109828 484.283435 667.527305 748.943837 709.920767 220.674655 194.524446 677.143280
... ... ... ... ... ... ... ... ... ...
Kilroot 33.287914 365.094251 272.739435 477.516539 544.616649 505.322093 150.719016 201.165723 507.420644
Belfast 20.903550 365.529754 276.165356 478.712132 544.169481 505.217054 168.043918 218.691134 509.620756
Kilkeel 69.325510 333.743937 256.699009 448.013287 507.775698 470.152416 225.848239 269.532038 481.962557
Larne 34.308180 377.853442 284.413852 489.963715 557.597214 518.205732 143.043362 196.596516 519.466143
Warren Point 62.130897 351.654630 275.022845 465.919464 525.111807 487.660757 229.023719 276.185606 500.043424

185 rows × 9 columns

In [ ]:
# re ordenando
seaports_uk_27700.set_index('portName').geometry.apply\
(lambda g: largeAirports_27700.set_index('name').geometry.distance(g)/1000).sort_index(axis=0).sort_index(axis=1)
Out[ ]:
name Belfast International Airport Birmingham International Airport Edinburgh Airport Glasgow International Airport London Gatwick Airport London Heathrow Airport London Luton Airport London Stansted Airport Manchester Airport
portName
Aberdeen 379.088614 523.017936 155.295960 202.857892 679.037078 640.666937 597.386799 604.735262 423.143398
Ardglass 58.353750 327.039697 235.851233 194.052532 504.421460 465.860022 440.868376 473.012680 242.329486
Ardrishaig 158.982469 464.015697 129.886301 65.555939 643.485327 603.105523 568.640397 591.739802 360.378459
Ardrossan 142.031163 408.393526 96.534318 34.499429 587.409431 546.998813 512.100552 534.888226 304.192189
Aultbea 355.905842 646.496167 249.224478 229.418749 821.210826 780.909995 742.397595 759.511194 540.683434
... ... ... ... ... ... ... ... ... ...
Whitstable 608.402759 226.361532 587.015034 617.732059 88.910403 104.651106 112.324828 79.890366 315.492874
Wick 462.381274 670.765330 277.002816 296.633585 831.498351 792.671100 749.971226 758.602266 568.066974
Wisbech 475.272768 130.838896 431.185021 464.904345 170.497504 139.517929 94.945912 87.163247 179.845160
Workington 167.167729 266.226746 154.871725 153.936736 445.156903 404.755758 370.354382 394.498375 161.975175
Yarmouth Harbour 543.441637 195.822379 597.202720 607.600643 104.607441 112.391099 152.635022 178.908880 299.491646

185 rows × 9 columns

In [ ]:
# guardando ese orden

distanceMatrixKM_sea_air= seaports_uk_27700.set_index('portName').geometry.apply\
                          (lambda g: largeAirports_27700.set_index('name').geometry.distance(g)/1000).\
                          sort_index(axis=0).sort_index(axis=1)
In [ ]:
# large airport más lejano a cada seaport

distanceMatrixKM_sea_air.idxmax(axis=1)
Out[ ]:
portName
Aberdeen                   London Gatwick Airport
Ardglass                   London Gatwick Airport
Ardrishaig                 London Gatwick Airport
Ardrossan                  London Gatwick Airport
Aultbea                    London Gatwick Airport
                                ...              
Whitstable          Glasgow International Airport
Wick                       London Gatwick Airport
Wisbech             Belfast International Airport
Workington                 London Gatwick Airport
Yarmouth Harbour    Glasgow International Airport
Length: 185, dtype: object
In [ ]:
# seaport más cercano a cada large airport

distanceMatrixKM_sea_air.idxmax(axis=0)
Out[ ]:
name
Belfast International Airport                     Sullom Voe
Birmingham International Airport                  Sullom Voe
Edinburgh Airport                   St. Mary's (Scilly Isl.)
Glasgow International Airport       St. Mary's (Scilly Isl.)
London Gatwick Airport                            Sullom Voe
London Heathrow Airport                           Sullom Voe
London Luton Airport                              Sullom Voe
London Stansted Airport                           Sullom Voe
Manchester Airport                                Sullom Voe
dtype: object
In [ ]:
# large airport más lejanno a cada seaport

distanceMatrixKM_sea_air.idxmin(axis=1)
Out[ ]:
portName
Aberdeen                        Edinburgh Airport
Ardglass            Belfast International Airport
Ardrishaig          Glasgow International Airport
Ardrossan           Glasgow International Airport
Aultbea             Glasgow International Airport
                                ...              
Whitstable                London Stansted Airport
Wick                            Edinburgh Airport
Wisbech                   London Stansted Airport
Workington          Glasgow International Airport
Yarmouth Harbour           London Gatwick Airport
Length: 185, dtype: object
In [ ]:
# seaport más cercano a cada large airport

distanceMatrixKM_sea_air.idxmin(axis=0)
Out[ ]:
name
Belfast International Airport                    Belfast
Birmingham International Airport               Sharpness
Edinburgh Airport                   Hound Point Terminal
Glasgow International Airport                    Renfrew
London Gatwick Airport                  Shoreham Harbour
London Heathrow Airport                           London
London Luton Airport                              London
London Stansted Airport                       Colchester
Manchester Airport                            Manchester
dtype: object

Escogiendo un seaport : Aberdeen¶

In [ ]:
distanceMatrixKM_sea_air.head(1)
Out[ ]:
name Belfast International Airport Birmingham International Airport Edinburgh Airport Glasgow International Airport London Gatwick Airport London Heathrow Airport London Luton Airport London Stansted Airport Manchester Airport
portName
Aberdeen 379.088614 523.017936 155.29596 202.857892 679.037078 640.666937 597.386799 604.735262 423.143398
In [ ]:
# escogiendo el seaport
aberdeen_distances = distanceMatrixKM_sea_air.loc['Aberdeen']
# asignando
closest_airport = aberdeen_distances.idxmin()
min_distance = aberdeen_distances.min()
In [ ]:
# cambiando los índices
seaports = seaports_uk_27700.set_index('portName')
airports = largeAirports_27700.set_index('name')
#filtrando
uk = country[country.COUNTRY=='United Kingdom']
In [ ]:
# plotting
base = uk.plot(facecolor="yellow", edgecolor='black', linewidth=0.4,figsize=(5,5))
seaports.loc[['Aberdeen']].plot(ax=base, color='green', marker='^', markersize=10, label='Aberdeen Port')
airports.loc[[closest_airport]].plot(ax=base, color='red', markersize=10, label=closest_airport)
plt.legend()
plt.show()
No description has been provided for this image

Exercise 2 (Distance between line and point)¶

  1. Use a map of points and a map of lines from your country.

  2. Compute the distance matrix for both.

  3. Select one line of the distance matrix, and plot the closests and the farthest point to that line.

In [ ]:
longest_rivers_27700 # mapa de líneas
Out[ ]:
name geometry length
2115 River Avon MULTILINESTRING ((416370.634 117850.427, 41637... 537927.236718
2761 River Thames MULTILINESTRING ((459675.517 180847.117, 45967... 350897.120340
2682 River Severn MULTILINESTRING ((373366.200 211267.798, 37329... 326450.311161
2722 River Stour MULTILINESTRING ((632950.555 158519.407, 63295... 318856.421150
2266 River Dee MULTILINESTRING ((331760.699 339517.608, 33180... 309110.609724
2853 River Wye MULTILINESTRING ((489602.377 186562.954, 48956... 292297.060518
2271 River Derwent MULTILINESTRING ((443855.667 331720.970, 44383... 283201.140930
2787 River Trent MULTILINESTRING ((398267.680 325421.202, 39827... 272575.103101
2282 River Don MULTILINESTRING ((429280.763 398799.321, 42927... 245148.653223
2758 River Test MULTILINESTRING ((435558.653 115739.584, 43556... 235050.804979
2365 River Frome MULTILINESTRING ((360223.492 96824.249, 360227... 228168.252531
2312 River Eden MULTILINESTRING ((540388.787 146717.695, 54040... 205348.214137
2397 River Great Ouse MULTILINESTRING ((518103.098 260199.826, 51809... 194204.635265
2713 River Spey MULTILINESTRING ((320115.428 841655.529, 32013... 168010.687478
2585 River Nene MULTILINESTRING ((478047.188 260040.295, 47810... 165166.228714
In [ ]:
# escogiendo un río
longest_rivers_27700[longest_rivers_27700.name.str.contains('Thames')]
Out[ ]:
name geometry length
2761 River Thames MULTILINESTRING ((459675.517 180847.117, 45967... 350897.12034
In [ ]:
#visualizando
longest_rivers_27700[longest_rivers_27700.name.str.contains('Thames')].plot()
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
# distancia a cada aeropuerto
longest_rivers_27700[longest_rivers_27700.name.str.contains('Thames')].iloc[0].geometry.distance(largeAirports_27700.set_index('name').geometry)/1000
Out[ ]:
name
Belfast International Airport       440.206968
Birmingham International Airport     79.511878
Manchester Airport                  185.333343
London Luton Airport                 40.919783
London Gatwick Airport               28.539257
London Heathrow Airport               5.598833
Glasgow International Airport       497.919525
Edinburgh Airport                   482.229332
London Stansted Airport              41.743305
Name: geometry, dtype: float64

Todas las posibles distancias de cada río largo a los aeropuertos grandes:¶

In [ ]:
# la matriz
distanceMatrixKM_riv_air=longest_rivers_27700.set_index('name').geometry.apply\
(lambda g: largeAirports_27700.set_index('name').geometry.distance(g)/1000).\
sort_index(axis=0).sort_index(axis=1)

distanceMatrixKM_riv_air
Out[ ]:
name Belfast International Airport Birmingham International Airport Edinburgh Airport Glasgow International Airport London Gatwick Airport London Heathrow Airport London Luton Airport London Stansted Airport Manchester Airport
name
River Avon 204.832193 18.823282 16.332744 34.221145 109.571731 89.798249 72.063676 101.411099 121.616137
River Dee 141.900989 98.656426 115.224845 105.479766 276.651455 237.691391 212.550712 245.905394 43.259932
River Derwent 171.135915 54.499779 140.022635 146.969543 206.777366 166.619803 128.515428 152.539451 34.795699
River Don 277.882507 105.375241 133.867534 160.139673 263.372339 223.544506 183.449420 202.175948 35.890745
River Eden 206.698068 179.236756 34.012540 78.797128 15.060416 39.890996 72.010013 68.980417 114.016321
River Frome 364.877891 59.075737 418.843229 424.983442 144.754641 115.190043 117.346256 158.887111 127.215096
River Great Ouse 445.671556 61.361576 428.414109 464.656183 108.450126 68.016294 28.257237 48.454331 162.288493
River Nene 433.571380 50.191716 419.658945 450.132336 128.198473 88.713213 48.868880 74.542595 147.524937
River Severn 294.947141 38.055529 356.886504 359.436763 163.701226 131.305683 124.197411 165.378931 74.459136
River Spey 280.719573 531.746361 129.260304 126.453902 703.369774 663.284611 623.614449 639.072550 426.117592
River Stour 360.402729 20.342625 394.266123 405.256879 102.938676 90.996409 62.423622 26.183620 97.477022
River Test 489.894116 129.697470 532.178540 544.641276 72.982743 59.617297 92.174546 124.467713 234.309594
River Thames 440.206968 79.511878 482.229332 497.919525 28.539257 5.598833 40.919783 41.743305 185.333343
River Trent 322.398569 30.802379 305.156928 342.094768 204.467210 164.070887 126.597230 146.999315 35.110733
River Wye 297.138203 76.636690 313.784405 332.912428 59.117954 19.860355 37.275871 70.950884 25.480614
In [ ]:
# ordenando las distancias de 'River Thames' a cada large airport

distanceMatrixKM_riv_air.loc['River Thames'].sort_values()
Out[ ]:
name
London Heathrow Airport               5.598833
London Gatwick Airport               28.539257
London Luton Airport                 40.919783
London Stansted Airport              41.743305
Birmingham International Airport     79.511878
Manchester Airport                  185.333343
Belfast International Airport       440.206968
Edinburgh Airport                   482.229332
Glasgow International Airport       497.919525
Name: River Thames, dtype: float64
In [ ]:
!pip install mapclassify

Collecting mapclassify
  Downloading mapclassify-2.6.1-py3-none-any.whl (38 kB)
Requirement already satisfied: networkx>=2.7 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (3.3)
Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (1.25.2)
Requirement already satisfied: pandas!=1.5.0,>=1.4 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (2.0.3)
Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (1.2.2)
Requirement already satisfied: scipy>=1.8 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (1.11.4)
Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas!=1.5.0,>=1.4->mapclassify) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=1.5.0,>=1.4->mapclassify) (2023.4)
Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=1.5.0,>=1.4->mapclassify) (2024.1)
Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->mapclassify) (1.4.2)
Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->mapclassify) (3.5.0)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas!=1.5.0,>=1.4->mapclassify) (1.16.0)
Installing collected packages: mapclassify
Successfully installed mapclassify-2.6.1
In [ ]:
# asignando
thames_river = distanceMatrixKM_riv_air.loc['River Thames']
min = thames_river.idxmin()
max = thames_river.idxmax()
In [ ]:
thames = longest_rivers_27700[longest_rivers_27700['name'] == 'River Thames'].copy()
closest_airport = largeAirports_27700[largeAirports_27700['name'] == min].copy()
farthest_airport = largeAirports_27700[largeAirports_27700['name'] == max].copy()
In [ ]:
# plotting : río Thames y sus aeropuertos más cercano y más lejano
base=thames.explore(color='blue')
closest_airport.explore(m=base, color='red', marker_kwds=dict(radius=5))
farthest_airport.explore(m=base, color='green', marker_kwds=dict(radius=5))
Out[ ]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Exercise 3 (Polygon to point)¶

  1. Create a HULL for some set of line map.

  2. Compute the distance matrix between the HULLS and a map of points.

  3. Plot the HULLS and the points. Show the closest and farthest points to the HULL.

Preparando los dos grupos : Norte y Sur¶

In [ ]:
country.centroid
Out[ ]:
0    POINT (348609.801 461152.220)
dtype: geometry
In [ ]:
longest_rivers_27700 # mapa de líneas
Out[ ]:
name geometry length
2115 River Avon MULTILINESTRING ((416370.634 117850.427, 41637... 537927.236718
2761 River Thames MULTILINESTRING ((459675.517 180847.117, 45967... 350897.120340
2682 River Severn MULTILINESTRING ((373366.200 211267.798, 37329... 326450.311161
2722 River Stour MULTILINESTRING ((632950.555 158519.407, 63295... 318856.421150
2266 River Dee MULTILINESTRING ((331760.699 339517.608, 33180... 309110.609724
2853 River Wye MULTILINESTRING ((489602.377 186562.954, 48956... 292297.060518
2271 River Derwent MULTILINESTRING ((443855.667 331720.970, 44383... 283201.140930
2787 River Trent MULTILINESTRING ((398267.680 325421.202, 39827... 272575.103101
2282 River Don MULTILINESTRING ((429280.763 398799.321, 42927... 245148.653223
2758 River Test MULTILINESTRING ((435558.653 115739.584, 43556... 235050.804979
2365 River Frome MULTILINESTRING ((360223.492 96824.249, 360227... 228168.252531
2312 River Eden MULTILINESTRING ((540388.787 146717.695, 54040... 205348.214137
2397 River Great Ouse MULTILINESTRING ((518103.098 260199.826, 51809... 194204.635265
2713 River Spey MULTILINESTRING ((320115.428 841655.529, 32013... 168010.687478
2585 River Nene MULTILINESTRING ((478047.188 260040.295, 47810... 165166.228714
In [ ]:
country.centroid.x.values[0],country.centroid.y.values[0]
Out[ ]:
(348609.8014521126, 461152.21989169234)
In [ ]:
# asignando valores
centroidX,centroidY=country.centroid.x.values[0],country.centroid.y.values[0]
In [ ]:
uk_LriverNorth = longest_rivers_27700[longest_rivers_27700.geometry.centroid.y >= centroidY] # norte
uk_LriverSouth = longest_rivers_27700[longest_rivers_27700.geometry.centroid.y < centroidY] # sur
In [ ]:
# plotting
fig, ax = plt.subplots(figsize=(10, 6))
uk_LriverNorth.plot(ax=ax, color='blue', alpha=0.8)
uk_LriverSouth.plot(ax=ax, color='greenyellow', alpha=0.8)
Out[ ]:
<Axes: >
No description has been provided for this image

Intentando arreglar los colores¶

In [ ]:
!pip install shapely

Requirement already satisfied: shapely in /usr/local/lib/python3.10/dist-packages (2.0.4)
Requirement already satisfied: numpy<3,>=1.14 in /usr/local/lib/python3.10/dist-packages (from shapely) (1.25.2)
In [ ]:
from shapely.geometry import LineString, MultiLineString, GeometryCollection
from shapely.ops import split
In [ ]:
centroidX, centroidY = country.centroid.x.values[0], country.centroid.y.values[0]
In [ ]:
# creando línea divisoria
division_line = LineString([(country.total_bounds[0], centroidY), (country.total_bounds[2], centroidY)])
In [ ]:
# creando listas
north_segments = []
south_segments = []
In [ ]:
# una funcion para dividir
def process_geometry(geom, division_line, centroidY):
    if geom.crosses(division_line):
        # dividir la geometría
        split_geoms = split(geom, division_line)
        # asegurarse de que split_geoms es una lista de geometrías
        if isinstance(split_geoms, (GeometryCollection, MultiLineString)):
            split_geoms = list(split_geoms.geoms)
        else:
            split_geoms = [split_geoms]
        for part in split_geoms:
            if part.centroid.y >= centroidY:
                north_segments.append(part)
            else:
                south_segments.append(part)
    else:
        if geom.centroid.y >= centroidY:
            north_segments.append(geom)
        else:
            south_segments.append(geom)
In [ ]:
# aplicándola
for geom in longest_rivers_27700.geometry:
    if isinstance(geom, MultiLineString):
        for part in geom.geoms:
            process_geometry(part, division_line, centroidY)
    else:
        process_geometry(geom, division_line, centroidY)
In [ ]:
north_gdf = gpd.GeoDataFrame(geometry=north_segments, crs=longest_rivers_27700.crs) # ríos norte
south_gdf = gpd.GeoDataFrame(geometry=south_segments, crs=longest_rivers_27700.crs) # ríos sur
In [ ]:
# plotting con los colores
fig, ax = plt.subplots(figsize=(10, 6))
north_gdf.plot(ax=ax, color='blue', alpha=0.8, label='Ríos del Norte')
south_gdf.plot(ax=ax, color='greenyellow', alpha=0.8, label='Ríos del Sur')
plt.axhline(y=centroidY, color='red', linestyle='--', linewidth=2, label='Línea de División')
plt.title('División de Segmentos de Ríos en Norte y Sur')
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left') # para que se vea el mapa
plt.show()
No description has been provided for this image

Continuando con el ejercicio¶

In [ ]:
north_gdf.dissolve().convex_hull.plot() # hull del norte
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
south_gdf.dissolve().convex_hull.plot() # hull del sur
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
uk_RNorth = north_gdf.unary_union.convex_hull
uk_RSouth = south_gdf.unary_union.convex_hull
In [ ]:
# plotting los hulls
fig, ax = plt.subplots(figsize=(10, 6))
gpd.GeoSeries(uk_RNorth).plot(ax=ax, color='blue', alpha=0.8, edgecolor='black')
gpd.GeoSeries(uk_RSouth).plot(ax=ax, color='greenyellow', alpha=0.8, edgecolor='black')
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
# combinando
combined_hulls = gpd.GeoDataFrame(
    {'region': ['north', 'south'],
     'geometry': [uk_RNorth, uk_RSouth]},
    crs="EPSG:27700"
).set_index('region')
In [ ]:
some_seaports = seaports_uk_27700.tail(8) # escogiendo solo unos seaports para el ejercicio, últimos 8

# creando la matriz
distanceMatrixKM_rivHull_sea = combined_hulls.geometry.apply(
    lambda g: some_seaports.set_index('portName').geometry.distance(g) / 1000
)
distanceMatrixKM_rivHull_sea
Out[ ]:
portName Sharpness Rothesay Dock Faslane Kilroot Belfast Kilkeel Larne Warren Point
region
north 276.814699 6.093190 29.279016 108.744108 122.826872 154.875247 111.903169 166.297680
south 0.000000 272.445464 302.092837 237.958183 238.541648 203.684299 250.563575 221.053637
In [ ]:
# plotting los hulls y los seaports escogidos
fig, ax = plt.subplots(figsize=(10, 6))
combined_hulls.plot(ax=ax, color=['blue', 'greenyellow'], alpha=0.6, edgecolor='black', legend=True)
some_seaports.plot(ax=ax, color='red', alpha=0.8, marker='^', markersize=5, label='Algunos puertos')
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
No description has been provided for this image
In [ ]:
mins=distanceMatrixKM_rivHull_sea.idxmin(axis="columns") # los mas cercanos
mins
Out[ ]:
region
north    Rothesay Dock
south        Sharpness
dtype: object
In [ ]:
# plotting
base=combined_hulls.explore()
some_seaports[some_seaports.portName.isin(mins)].explore(m=base,color='yellow',marker_kwds=dict(radius=15)) #closest
some_seaports[~some_seaports.portName.isin(mins)].explore(m=base,color='crimson',marker_kwds=dict(radius=5)) #farthest
Out[ ]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Exercise 4¶

  1. Select a line map and a point one.

  2. Get the buffer for the lines, select a distance.

  3. Keep the points that are within the buffer (you might need to play with differnt distances until you show something interesting).

Longest Rivers y Small Airports:¶

In [ ]:
distanceMatrixKM_riv_air.loc['River Thames'].min()
minD=distanceMatrixKM_riv_air.loc['River Thames'].min()*1000 # definiendo el buffer
In [ ]:
longest_rivers_27700[longest_rivers_27700.name.str.contains('Thames')] # escogiendo el río
Out[ ]:
name geometry length
2761 River Thames MULTILINESTRING ((459675.517 180847.117, 45967... 350897.12034
In [ ]:
longest_rivers_27700[longest_rivers_27700.name=='River Thames'].buffer(distance = minD) # buffer
Out[ ]:
2761    POLYGON ((407160.536 187786.469, 406714.779 18...
dtype: geometry
In [ ]:
bufferAroundThames=rivers_27700[rivers_27700.name=='River Thames'].buffer(distance = minD)
bufferAsBase=bufferAroundThames.explore(color='red')
In [ ]:
# visualizando
longest_rivers_27700[longest_rivers_27700.name=='River Thames'].explore(m=bufferAsBase,color='blue',style_kwds={'weight':0.5})
smallAirports.explore(m=bufferAsBase,color='black')
Out[ ]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [ ]:
# todos los small airports que estan en el buffer
riversWithinBuffer=smallAirports.clip(mask=bufferAroundThames)
riversWithinBuffer
Out[ ]:
name kind latitude_deg longitude_deg elevation_ft iso_region municipality geometry
203 Oaksey Park Airport small_airport 51.632198 -2.014720 250.0 GB-ENG Malmesbury POINT (399077.857 192598.714)
286 Steventon Private Airstrip small_airport 51.621133 -1.333915 NaN GB-ENG None POINT (446209.909 191577.892)
1054 Hamilton House Airstrip small_airport 51.663785 -1.446633 NaN GB-ENG Charney POINT (438370.565 196256.496)
781 Inglesham Private Airstrip small_airport 51.669934 -1.716056 NaN GB-ENG None POINT (419733.200 196833.647)
205 RAF Abingdon small_airport 51.690300 -1.316670 259.0 GB-ENG Abingdon POINT (447331.846 199281.506)
913 South Cerney Airfield small_airport 51.687200 -1.920400 364.0 GB-ENG South Cerney (Glos) POINT (405599.453 198718.853)
113 White Waltham Airfield small_airport 51.500801 -0.774444 131.0 GB-ENG Maidenhead POINT (485165.383 178696.654)
628 Whittles Farm Airstrip small_airport 51.500485 -1.035757 NaN GB-ENG None POINT (467027.918 178390.240)
634 North Moreton Airstrip small_airport 51.603506 -1.184651 NaN GB-ENG None POINT (456565.210 189722.177)
549 Drayton St. Leonard small_airport 51.664100 -1.126930 NaN GB-ENG None POINT (460482.224 196507.362)
843 Harpsden Park Airstrip small_airport 51.519700 -0.896900 NaN GB-ENG None POINT (476633.999 180663.278)
642 Luxter Farm Airstrip small_airport 51.595970 -0.892864 NaN GB-ENG None POINT (476785.631 189149.722)
192 Wycombe Air Park small_airport 51.611698 -0.808333 515.0 GB-ENG High Wycombe POINT (482612.258 190990.936)
123 Damyns Hall Aerodrome small_airport 51.528599 0.245556 59.0 GB-ENG Upminster POINT (555864.860 183466.251)
886 Gerpins Farm Airstrip small_airport 51.531300 0.231000 NaN GB-ENG Upminster POINT (554846.105 183735.731)
In [ ]:
# plotting small airports within buffer
bufferAsBase=bufferAroundThames.explore(color='red')
longest_rivers_27700[longest_rivers_27700.name=='River Thames'].explore(m=bufferAsBase,color='blue',style_kwds={'weight':0.5})
riversWithinBuffer.explore(m=bufferAsBase,color='black')
Out[ ]:
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In [ ]:
distanceMatrixKM_riv_air.min(axis=1).min()
Out[ ]:
5.598833353982658
In [ ]:
# usando el valor anterior
minMin=distanceMatrixKM_riv_air.min(axis=1).min()*100
In [ ]:
# visualizando
MinBuffer=longest_rivers_27700.buffer(distance = minMin).explore(color='red')
longest_rivers_27700.explore(m=MinBuffer,color='blue',style_kwds={'weight':0.5})
Out[ ]:
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In [ ]:
longrivers_buff=longest_rivers_27700.buffer(distance = minMin)
longrivers_buff
Out[ ]:
2115    MULTIPOLYGON (((465726.993 279235.707, 465724....
2761    POLYGON ((403864.963 194697.028, 403897.795 19...
2682    MULTIPOLYGON (((284490.555 287807.677, 284497....
2722    MULTIPOLYGON (((627240.775 163869.125, 627256....
2266    MULTIPOLYGON (((298472.831 797422.399, 298500....
2853    MULTIPOLYGON (((489086.006 186951.192, 489086....
2271    MULTIPOLYGON (((467502.779 428650.788, 467505....
2787    MULTIPOLYGON (((396071.449 327403.325, 396052....
2282    MULTIPOLYGON (((411665.387 402437.158, 411684....
2758    POLYGON ((418276.432 128249.653, 418276.212 12...
2365    MULTIPOLYGON (((374602.856 209932.918, 374603....
2312    MULTIPOLYGON (((538401.423 151891.251, 538401....
2397    MULTIPOLYGON (((560736.034 318825.671, 560853....
2713    MULTIPOLYGON (((284634.027 807159.929, 284680....
2585    MULTIPOLYGON (((500194.978 282407.481, 500194....
dtype: geometry
In [ ]:
# todos los small aiports que estan cercanos a los ríos más largos (dentro del buffer definido)
longRiversWithinBuffs=smallAirports.clip(longrivers_buff)
longRiversWithinBuffs
Out[ ]:
name kind latitude_deg longitude_deg elevation_ft iso_region municipality geometry
743 Marsh Court Manor Private Airstrip small_airport 51.098725 -1.495085 NaN GB-ENG None POINT (435451.848 133389.999)
1125 Lydeway Field small_airport 51.194800 -1.553000 350.0 GB-ENG None POINT (431331.641 144048.214)
89 Thruxton Aerodrome small_airport 51.210602 -1.600000 319.0 GB-ENG Andover POINT (428037.864 145786.632)
608 Avon Lane Airfield small_airport 51.410210 -2.447792 NaN GB-ENG Saltford POINT (368952.225 168005.027)
781 Inglesham Private Airstrip small_airport 51.669934 -1.716056 NaN GB-ENG None POINT (419733.200 196833.647)
622 Bycross Farm Airstrip small_airport 52.077944 -2.907441 NaN GB-ENG Moccas POINT (337907.625 242564.663)
456 Breidden Airfield small_airport 52.702036 -3.092449 NaN GB-WLS Welshpool POINT (326277.736 312156.017)
947 Grove Field Airstrip small_airport 52.229000 -1.618530 NaN GB-ENG None POINT (426151.926 259046.221)
756 Bericote Farm Airstrip small_airport 52.327190 -1.547935 NaN GB-ENG Blackdown / Ashow POINT (430905.530 269995.654)
245 Aboyne Airfield small_airport 57.075556 -2.839444 463.0 GB-SCT Aboyne POINT (349207.111 798594.668)
637 Earwig Farm Airstrip small_airport 52.185021 -0.515957 NaN GB-ENG None POINT (501556.928 255123.742)
844 East Haxted Farm Airstrip small_airport 51.187884 0.038667 NaN GB-ENG None POINT (542568.189 145159.119)
588 Titsey Airstrip small_airport 51.275600 0.026756 NaN GB-ENG None POINT (541466.754 154889.816)
754 Crosbie Field UL small_airport 52.012700 0.747500 NaN GB-ENG Little Cornard POINT (588648.916 238482.607)
885 Pentlow Airstrip small_airport 52.077436 0.623796 NaN GB-ENG Pentlow (Essex) POINT (579900.897 245367.394)
343 Thornborough Grounds Airstrip small_airport 52.005743 -0.935664 270.0 GB-ENG Bourton POINT (473155.865 234679.772)
651 Rectory Farm Airstrip small_airport 53.086138 -0.848486 NaN GB-ENG Averham POINT (477222.755 354943.979)
1080 Low Moor Farm Airstrip small_airport 54.182606 -0.731792 NaN GB-ENG None POINT (482864.269 477056.757)
792 Ings Farm Airstrip small_airport 54.204900 -0.616000 NaN GB-ENG Yedingham POINT (490372.122 479678.940)
In [ ]:
# plotting simple
base=longrivers_buff.plot(color='orange')
longRiversWithinBuffs.plot(ax=base, color='green', markersize=3)
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
# visaulizando en mapa
base=longrivers_buff.explore(color='orange')
longRiversWithinBuffs.explore(m=base, color='green')
Out[ ]:
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